Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
72
datasets available to search
ShareScore release 0.7.1
Dataset results
72 results for “limnology”
North Temperate Lakes LTER: Physical Limnology of Primary Study Lakes 1981 - current
Parameters characterizing the physical limnology of the eleven primary lakes (Allequash, Big Muskellunge, Crystal, Sparkling, Trout, bog lakes 27-02 [Crystal Bog] and 12-15 [Trout Bog], Mendota, Monona, Wingra and Fish) are measured at one station in the deepest part of each lake at 0.25 m to 1 m depth intervals depending on the lake. Measured parameters in the data set include water temperature, vertical penetration of photosynthetically active radiation (PAR), dissolved oxygen in mg/L and percent saturation. Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season for the northern lakes. The southern lakes are similar except that sampling occurs monthly during the fall and typically only once during the winter (depending on ice conditions). Number of sites: 11. More information on NTL’s primary study lakes can be found at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-ntl&identifier=434. Please consult NTL's website (https://lter.limnology.wisc.edu) for information on experimental lake manipulations and the Wisconsin DNR's website (https://dnr.wisconsin.gov/) for management activities.
North Temperate Lakes LTER: Physical and Chemical Limnology of Lake Kegonsa and Lake Waubesa 1994 - current
Physical and chemicals parameters of two Madison-area lakes in the Yahara chain not included as core NTL-LTER study lakes. Parameters include intermittently sampled water temperature, dissolved oxygen, ph, total alkalinity, chloride and sulfate. Nutrient data has been collected since 2015. Number of sites: 2.
Supplementary dataset to the publication "Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography"
<p>The NetCDF data files contain the training dataset used to develop the Optical Water Type (OWT) framework proposed by Bi and Hieronymi (2024). The dataset is available in two spectral versions:</p> <p> 1. <code>owt_BH2024_training_data_hyper.nc</code>: This file includes training data with a spectral resolution of 2 nm, ranging from 400 to 900 nm.<br> 2. <code>owt_BH2024_training_data_olci.nc</code>: This file contains data formatted similarly to the hyperspectral version but aligned with the nominal Sentinel-3 OLCI wavebands.</p> <h2>Contents of the Dataset</h2> <p>For each version, the dataset includes spectral inherent and apparent optical properties such as:</p> <p> • Remote Sensing Reflectance (Rrs)<br> • Pure Water Absorption (aw)<br> • Absorption Coefficient of Detritus (ad)<br> • Total Absorption Coefficient without Pure Water (agp)<br> • Absorption Coefficient of Phytoplankton (aph)<br> • Backscattering Coefficient of Total Particulate Matter (bbp)<br> • Scattering Coefficient of Total Particulate Matter (bp)<br> • Scattering Coefficient of Pure Water (bw)</p> <p>Additionally, the dataset includes various environmental and biological parameters:</p> <p> • Chlorophyll a Concentration (Chl)<br> • Inorganic Suspended Matter Concentration (ISM)<br> • Colored Dissolved Organic Matter Absorption at 440 nm (ag440)<br> • Single-Scattering Albedo of Detritus at 550 nm (A_d)<br> • Power Law Exponent of Detritus Attenuation (G_d)<br> • Water Salinity (Sal)<br> • Water Temperature (Temp)<br> • Fraction for Diminished Coccolithophore Absorption (a_frac)<br> • Fraction of Coccolithophore Group (cocco_frac)</p> <h2>Optical Water Types</h2> <p>The training dataset includes 10 pre-defined optical water types, with 10,000 samples for each type. Detailed descriptions of these water types can be found in Table 1 of Bi and Hieronymi (2024) or as follows,</p> <table> <tbody> <tr> <td>OWT</td> <td>Desciption</td> </tr> <tr> <td>1</td> <td>Extremely clear and oligotrophic indigo-blue waters with high reflectance in the short visible wavelengths.</td> </tr> <tr> <td>2</td> <td>Blue waters with similar biomass level as OWT 1 but with slightly higher detritus and CDOM content.</td> </tr> <tr> <td>3a</td> <td>Turquoise waters with slightly higher phytoplankton, detritus, and CDOM compared to the first two types.</td> </tr> <tr> <td>3b</td> <td>A special case of OWT 3a with similar detritus and CDOM distribution but with strong scattering and little absorbing particles like in the case of Coccolithophore blooms. This type usually appears brighter and exhibits a remarkable ~490 nm reflectance peak.</td> </tr> <tr> <td>4a</td> <td>Greenish water found in coastal and inland environments, with higher biomass compared to the previous water types. Reflectance in short wavelengths is usually depressed by the absorption of particles and CDOM.</td> </tr> <tr> <td>4b</td> <td>A special case of OWT 4a, sharing similar detritus and CDOM distribution, exhibiting phytoplankton blooms with higher scattering coefficients, e.g., Coccolithophore bloom. The color of this type shows a very bright green.</td> </tr> <tr> <td>5a</td> <td>Green eutrophic water, with significantly higher phytoplankton biomass, exhibiting a bimodal reflectance shape with typical peaks at ~560 and ~709 nm.</td> </tr> <tr> <td>5b</td> <td>Green hyper-eutrophic water, with even higher biomass than that of OWT 5a (over several orders of magnitude), displaying a reflectance plateau in the Near Infrared Region, NIR (vegetation-like spectrum).</td> </tr> <tr> <td>6</td> <td>Bright brown water with high detritus concentrations, which has a high reflectance determined by scattering.</td> </tr> <tr> <td>7</td> <td>Dark brown to black water with very high CDOM concentration, which has low reflectance in the entire visible range and is dominated by absorption.</td> </tr> </tbody> </table> <h2>Additional Information</h2> <p>The detailed description of the data simulation can be found in the supporting information of Bi and Hieronymi (2024). The models used for simulating the data are available on GitHub:</p> <p> • Component IOP Model: <a href="https://github.com/bishun945/IOPmodel" target="_blank" rel="noopener">Bio-geo-optical modelling of natural waters by Bi, Hieronymi, and Röttgers (2023)</a><br> • OWT Package: <a href="https://github.com/bishun945/pyOWT" target="_blank" rel="noopener">pyOWT</a></p> <h2>References</h2> <p> 1. OWT Framework: Bi, S., and Hieronymi, M. (2024). Holistic optical water type classification for ocean, coastal, and inland waters. Limnology & Oceanography, lno.12606. doi: 10.1002/lno.12606<br> 2. Component IOP Model: Bi, S., Hieronymi, M., and Röttgers, R. (2023). Bio-geo-optical modelling of natural waters. Front. Mar. Sci. 10, 1196352. doi: 10.3389/fmars.2023.1196352<br> 3. Pure Water IOP Model: Röttgers, R., Doerffer, R., McKee, D., and Schönfeld, W. (2016). The Water Optical Properties Processor (WOPP): Pure Water Spectral Absorption, Scattering and Real Part of Refractive Index Model. Technical Report No WOPP-ATBD/WRD6. Available at: https://calvalportal.ceos.org/tools<br> 4. Rrs Model: Lee, Z., Du, K., Voss, K. J., Zibordi, G., Lubac, B., Arnone, R., et al. (2011). An inherent-optical-property-centered approach to correct the angular effects in water-leaving radiance. Appl. Opt. 50, 3155. doi: 10.1364/AO.50.003155</p> <h2>Authors and Contact</h2> <p> • Author: Shun Bi, Martin Hieronymi, Rüdiger Röttgers<br> • Creator: Shun Bi, Shun.Bi@hereon.de</p> <h2>Example Python Code to Read Data</h2> <p>Here is an example of how to read the NetCDF data using Python and the <code>xarray</code> library:</p> <pre><code>import xarray as xr # Load the dataset data_hyper = xr.open_dataset("path_to_your_file/owt_BH2024_training_data_hyper.nc") # Print the dataset to see its structure print(data_hyper) # Access a specific variable, e.g., remote sensing reflectance (Rrs) rrs = data_hyper['Rrs'] # Plot a sample of Rrs import matplotlib.pyplot as plt # Select a sample ID, for example the first sample sample_id = 0 plt.plot(data_hyper['wavelen'], rrs[sample_id, :]) plt.xlabel('Wavelength (nm)') plt.ylabel('Rrs (1/sr)') plt.title(f'Remote Sensing Reflectance for Sample ID {sample_id}') plt.show()</code></pre>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Limnological data for 17 mountain lakes in Banff and Yoho National Parks (Canadian Rocky Mountains) from 2015 to 2022
From 2015 to 2022, mid-summer vertical profiles of temperature, chlorophyll a fluorescence, turbidity, and fDOM were collected in a set of 17 lakes in Banff and Yoho National Parks, Canada. These lakes are located across montane, sub-alpine and alpine ecoregions and they vary widely in elevation (1300-2423 m a.s.l.), surface area (1.5-116 ha) and maximum depth (2.4-39.2 m). Eight of the lakes receive surface and/or groundwater hydrologic inputs from glaciers within the catchment, and the other nine lakes are not glacially-fed. Vertical profiles were collected in each lake within one or two days of an index sampling date between late July and early August using an Exo2 vertical profiling sonde. Measurements were taken at 1 s intervals as the sonde was lowered slowly through the water column, and then averaged over 0.5 m depth intervals. In addition, attenuation rates were estimated for 305 nm, 320 nm, and 380 nm, and PAR (400-700 nm) as the slopes of log-linear regressions of irradiance vs. depth. Downwelling irradiance measured with a Biospherical Instruments underwater radiometer. Vertical Profile Data are contained in Can_Rocky_Mtn_Lakes_Profiles.csv. Attenuation rates are contained in Can_Rocky_Mtn_Lakes_Kd.csv. Information about study lakes is contained in Can_Rocky_Mtn_Lakes_Site_Information.csv.
MCR LTER: Coral Reef: Coral Growth in Temperature and Alkalinity Treatments: Edmunds 2011 Limnology & Oceanography
These data were generated from a one-time experiment in support of a coral ecophysiology manuscript; published in Edmunds, PJ (2011) Limnology and Oceanography 56: 2402-2410 doi: 10.4319/lo.2011.56.6.2402 I tested the hypothesis that the effects of high pCO2 and temperature on massive Porites spp. (Scleractinia) are modified by heterotrophic feeding (zooplanktivory). Small colonies of massive Porites spp. from the back reef of Moorea, French Polynesia, were incubated for 1 month under combinations of temperature (29.3 C vs. 25.6 C), pCO2 (41.6 vs. 81.5 Pa), and feeding regimes (none vs. ad libitum access to live Artemia spp.), with the response assessed using calcification and biomass. Area-normalized calcification was unaffected by pCO2, temperature, and the interaction between the two, although it increased 40% with feeding. Biomass increased 35% with feeding and tended to be higher at 25.6 C compared to 29.3 C, and as a result, biomass-normalized calcification statistically was unaffected by feeding, but was depressed 12-17% by high pCO2, with the effect accentuated at 25.6 C. These results show that massive Porites spp. has the capacity to resist the effects on calcification of 1 month exposure to 81.5 Pa pCO2 through heterotrophy and changes in biomass. Area-normalized calcification is sustained at high pCO2 by a greater biomass with a reduced biomass-normalized rate of calcification. This mechanism may play a role in determining the extent to which corals can resist the long-term effects of ocean acidification.
Abb. 1 in Nachruf Roland Pechlaner (1934-2022) international anerkannter Limnologe und Fischforscher
Abb. 1: Übersicht Vorderer Finstertaler See gegen Süden im Sommer 1962 (Pfeil zeigt Position der Forschungsstation) (Foto aus der Sammlung Risch-Lau, Vorarlberger Landesmuseum, Bregenz).
Abb. 5 in Nachruf Roland Pechlaner (1934-2022) international anerkannter Limnologe und Fischforscher
Abb. 5: Univ. Prof. Dr. Roland Pechlaner (rechts) mit Studierenden bei Alzexkursion 1996 (Foto E. Rott).
Abb. 7 in Nachruf Roland Pechlaner (1934-2022) international anerkannter Limnologe und Fischforscher
Abb. 7: Univ. Prof. Dr. Roland Pechlaner und Gattin Dr. Christine Pechlaner bei Feier zum 85. Geburtstag (Foto E. Rott).
Abb. 2 in Nachruf Roland Pechlaner (1934-2022) international anerkannter Limnologe und Fischforscher
Abb. 2: Übersicht Piburger See mit Seebichlhof (Pfeil), Schonbucht gegen Tschirgant (gegen NW) im Spätherbst 2020 (Foto E. Rott).
Figure 1 in Taxonomic, morphometric and limnological assessment of the commercially important ichthyofauna of Sakhakot Stream, Malakand, Pakistan
Figure 1. Study area, District Malakand, Pakistan, where sample collection was carried out. A. Sakhakot Stream, B. Dargai Stream, C. Shergarh Stream.
Figure 4 in Limnological variables associated with the presence of Anopheles Meigen, 1818 (Diptera: Culicidae) larvae in breeding sites in Amazonas, Brazil
Figure 4. Principal Component Analysis (PCA) between limnological variables and anopheline composition in larval habitats. pH; turbidity; nitrate; phosphate; TSS (total suspended solids); cond (conductivity); OD (dissolved oxygen); temp (temperature). / Análisis de Componentes Principales (ACP) entre variables limnológicas y composición de anofelinos en hábitats larvarios. pH; turbidez; nitrato; fosfato; SST (sólidos suspendidos totales); cond (conductividad); OD (oxÍgeno disuelto); temp (temperatura).
Figure 3 in Limnological variables associated with the presence of Anopheles Meigen, 1818 (Diptera: Culicidae) larvae in breeding sites in Amazonas, Brazil
Figure 3. Similarity dendrogram of the larval habitats (A), and Principal Component Analysis – PCA (B) involving the limnological variables from artificial larval habitats of anophelines. Abbreviations: tss (total suspended solids); turb (turbidity); cond (conductivity); do (dissolved oxygen); temp (temperature); nitra (nitrate); phos (phosphate); larval habitats: B1, B2, B3, T4, T5, T6, T7, P9, P9, P10. / Dendrograma de similitud de los hábitats larvarios (A), y Análisis de Componentes Principales - ACP (B) que incluye las variables limnológicas de los hábitats larvarios artificiales de anofelinos. Abreviaturas: tss (sólidos suspendidos totales); turb (turbidez); cond (conductividad); do (oxÍgeno disuelto); temp (temperatura); nitra (nitrato); phos (fosfato); hábitats larvarios: B1, B2, B3, T4, T5, T6, T7, P9, P9, P10.
Figure 2 in Limnological variables associated with the presence of Anopheles Meigen, 1818 (Diptera: Culicidae) larvae in breeding sites in Amazonas, Brazil
Figure 2. Observed relationship between larval habitat characteristics and anopheline composition in breeding sites in the metropolitan area of Manaus, Amazonas. / Relación observada entre las caracterÍsticas del hábitat larval y la composición de anofelinos en sitios de reproducción en el área metropolitana de Manaus, Amazonas.
Figure 1 in Limnological variables associated with the presence of Anopheles Meigen, 1818 (Diptera: Culicidae) larvae in breeding sites in Amazonas, Brazil
Figure 1. Distribution of breeding sites in the peri-urban area of the metropolitan region of Manaus, Amazonas, Brazil. / Distribución de los criaderos en el área periurbana de la región metropolitana de Manaus, Amazonas, Brasil.
Figure 12 in A limnological reconnaissance of the Falkland Islands; with particular reference to the waterfleas (Arthropoda: Anomopoda)
Figure 12. Bosmina (Neobosmina) chilensis, female, position of lateral head pore. Scale bar: 0.5 mm.
Abb. 4 in Nachruf Roland Pechlaner (1934-2022) international anerkannter Limnologe und Fischforscher
Abb. 4: Langsam fliessender Mäander an der Alz in Bayern im Herbst 1996 (Foto E. Rott).
An Overview of Limnological Studies in Brazilian Fish Farms - Supplementary Material
<p>Este repositório contém uma planilha Excel com dados complementares para o artigo <strong>"Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras".</strong> A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.</p>
Supplementary Material - An overview of limnological studies in Brazilian fish farms
<p>Este repositório contém uma planilha Excel com dados complementares para o artigo <strong>"Uma visão geral dos estudos limnológicos nas pisciculturas brasileiras".</strong> A planilha inclui informações sobre a qualidade da água e parâmetros limnológicos observados em pisciculturas no Brasil, conforme discutido no artigo. Os dados abrangem variáveis como temperatura, pH, oxigênio dissolvido, entre outras, coletadas em diversas pisciculturas espalhadas pelo país.</p>
Figure 21 in A limnological reconnaissance of the Falkland Islands; with particular reference to the waterfleas (Arthropoda: Anomopoda)
Figure 21. Paralona pigra, female, post-abdomen.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.